Ontology type: schema:Chapter
2012
AUTHORSJames Ting-Ho Lo , Yichuan Gui , Yun Peng
ABSTRACTA method of training multilayer perceptrons (MLPs) to reach a global or nearly global minimum of the standard mean squared error (MSE) criterion is proposed. It has been found that the region in the weight space that does not have a local minimum of the normalized risk-averting error (NRAE) criterion expands strictly to the entire weight space as the risk-sensitivity index increases to infinity. If the MLP under training has enough hidden neurons, the MSE and NRAE criteria are both equal to nearly zero at a global or nearly global minimum. Training the MLP with the NRAE at a sufficiently large risk-sensitivity index can therefore effectively avoid non-global local minima. Numerical experiments show consistently successful convergence from different initial guesses of the weights of the MLP at a risk-sensitivity index over 106. The experiments are conducted on examples with non-global local minima of the MSE criterion that are difficult to escape from by training directly with the MSE criterion. More... »
PAGES440-447
Advances in Neural Networks – ISNN 2012
ISBN
978-3-642-31345-5
978-3-642-31346-2
http://scigraph.springernature.com/pub.10.1007/978-3-642-31346-2_50
DOIhttp://dx.doi.org/10.1007/978-3-642-31346-2_50
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1035628918
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Medical and Health Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1103",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Clinical Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Department of Mathematics and Statistics, University of Maryland, Baltimore County, 21250, Baltimore, Maryland, USA",
"id": "http://www.grid.ac/institutes/grid.266673.0",
"name": [
"Department of Mathematics and Statistics, University of Maryland, Baltimore County, 21250, Baltimore, Maryland, USA"
],
"type": "Organization"
},
"familyName": "Lo",
"givenName": "James Ting-Ho",
"id": "sg:person.013512526631.81",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013512526631.81"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 21250, Baltimore, Maryland, USA",
"id": "http://www.grid.ac/institutes/grid.266673.0",
"name": [
"Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 21250, Baltimore, Maryland, USA"
],
"type": "Organization"
},
"familyName": "Gui",
"givenName": "Yichuan",
"id": "sg:person.014111634523.11",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014111634523.11"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 21250, Baltimore, Maryland, USA",
"id": "http://www.grid.ac/institutes/grid.266673.0",
"name": [
"Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 21250, Baltimore, Maryland, USA"
],
"type": "Organization"
},
"familyName": "Peng",
"givenName": "Yun",
"id": "sg:person.01136741416.72",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136741416.72"
],
"type": "Person"
}
],
"datePublished": "2012",
"datePublishedReg": "2012-01-01",
"description": "A method of training multilayer perceptrons (MLPs) to reach a global or nearly global minimum of the standard mean squared error (MSE) criterion is proposed. It has been found that the region in the weight space that does not have a local minimum of the normalized risk-averting error (NRAE) criterion expands strictly to the entire weight space as the risk-sensitivity index increases to infinity. If the MLP under training has enough hidden neurons, the MSE and NRAE criteria are both equal to nearly zero at a global or nearly global minimum. Training the MLP with the NRAE at a sufficiently large risk-sensitivity index can therefore effectively avoid non-global local minima. Numerical experiments show consistently successful convergence from different initial guesses of the weights of the MLP at a risk-sensitivity index over 106. The experiments are conducted on examples with non-global local minima of the MSE criterion that are difficult to escape from by training directly with the MSE criterion.",
"editor": [
{
"familyName": "Wang",
"givenName": "Jun",
"type": "Person"
},
{
"familyName": "Yen",
"givenName": "Gary G.",
"type": "Person"
},
{
"familyName": "Polycarpou",
"givenName": "Marios M.",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-3-642-31346-2_50",
"inLanguage": "en",
"isAccessibleForFree": false,
"isPartOf": {
"isbn": [
"978-3-642-31345-5",
"978-3-642-31346-2"
],
"name": "Advances in Neural Networks \u2013 ISNN 2012",
"type": "Book"
},
"keywords": [
"criteria",
"neurons",
"training",
"index",
"index increases",
"increase",
"weight",
"training methods",
"method",
"minimum",
"region",
"risk-sensitivity index",
"NRAE",
"experiments",
"problem",
"multilayer perceptron",
"space",
"perceptron",
"weight space",
"successful convergence",
"example",
"global minimum",
"mean squared error criterion",
"squared error criterion",
"error criterion",
"local minima",
"infinity",
"MSE",
"numerical experiments",
"convergence",
"different initial guesses",
"initial guess",
"guess",
"MSE criterion",
"minimum problem",
"risk-averting error criterion"
],
"name": "Overcoming the Local-Minimum Problem in Training Multilayer Perceptrons with the NRAE Training Method",
"pagination": "440-447",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1035628918"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-3-642-31346-2_50"
]
}
],
"publisher": {
"name": "Springer Nature",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-3-642-31346-2_50",
"https://app.dimensions.ai/details/publication/pub.1035628918"
],
"sdDataset": "chapters",
"sdDatePublished": "2022-06-01T22:36",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/chapter/chapter_451.jsonl",
"type": "Chapter",
"url": "https://doi.org/10.1007/978-3-642-31346-2_50"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-31346-2_50'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-31346-2_50'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-31346-2_50'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-31346-2_50'
This table displays all metadata directly associated to this object as RDF triples.
122 TRIPLES
23 PREDICATES
62 URIs
55 LITERALS
7 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/978-3-642-31346-2_50 | schema:about | anzsrc-for:11 |
2 | ″ | ″ | anzsrc-for:1103 |
3 | ″ | schema:author | N7349809fe66e41ce8326efecc7214c1d |
4 | ″ | schema:datePublished | 2012 |
5 | ″ | schema:datePublishedReg | 2012-01-01 |
6 | ″ | schema:description | A method of training multilayer perceptrons (MLPs) to reach a global or nearly global minimum of the standard mean squared error (MSE) criterion is proposed. It has been found that the region in the weight space that does not have a local minimum of the normalized risk-averting error (NRAE) criterion expands strictly to the entire weight space as the risk-sensitivity index increases to infinity. If the MLP under training has enough hidden neurons, the MSE and NRAE criteria are both equal to nearly zero at a global or nearly global minimum. Training the MLP with the NRAE at a sufficiently large risk-sensitivity index can therefore effectively avoid non-global local minima. Numerical experiments show consistently successful convergence from different initial guesses of the weights of the MLP at a risk-sensitivity index over 106. The experiments are conducted on examples with non-global local minima of the MSE criterion that are difficult to escape from by training directly with the MSE criterion. |
7 | ″ | schema:editor | Na3e257bfec26458c9678ce7a229bb1c4 |
8 | ″ | schema:genre | chapter |
9 | ″ | schema:inLanguage | en |
10 | ″ | schema:isAccessibleForFree | false |
11 | ″ | schema:isPartOf | N785429d281a14d048b0ff39ca1e34ff2 |
12 | ″ | schema:keywords | MSE |
13 | ″ | ″ | MSE criterion |
14 | ″ | ″ | NRAE |
15 | ″ | ″ | convergence |
16 | ″ | ″ | criteria |
17 | ″ | ″ | different initial guesses |
18 | ″ | ″ | error criterion |
19 | ″ | ″ | example |
20 | ″ | ″ | experiments |
21 | ″ | ″ | global minimum |
22 | ″ | ″ | guess |
23 | ″ | ″ | increase |
24 | ″ | ″ | index |
25 | ″ | ″ | index increases |
26 | ″ | ″ | infinity |
27 | ″ | ″ | initial guess |
28 | ″ | ″ | local minima |
29 | ″ | ″ | mean squared error criterion |
30 | ″ | ″ | method |
31 | ″ | ″ | minimum |
32 | ″ | ″ | minimum problem |
33 | ″ | ″ | multilayer perceptron |
34 | ″ | ″ | neurons |
35 | ″ | ″ | numerical experiments |
36 | ″ | ″ | perceptron |
37 | ″ | ″ | problem |
38 | ″ | ″ | region |
39 | ″ | ″ | risk-averting error criterion |
40 | ″ | ″ | risk-sensitivity index |
41 | ″ | ″ | space |
42 | ″ | ″ | squared error criterion |
43 | ″ | ″ | successful convergence |
44 | ″ | ″ | training |
45 | ″ | ″ | training methods |
46 | ″ | ″ | weight |
47 | ″ | ″ | weight space |
48 | ″ | schema:name | Overcoming the Local-Minimum Problem in Training Multilayer Perceptrons with the NRAE Training Method |
49 | ″ | schema:pagination | 440-447 |
50 | ″ | schema:productId | N2ed4669085014871ac2ab93026a51dab |
51 | ″ | ″ | N9bc7eaea5da24af8a1eac3d9888c6537 |
52 | ″ | schema:publisher | N8d034199143849b384c592f5c5e87f2e |
53 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1035628918 |
54 | ″ | ″ | https://doi.org/10.1007/978-3-642-31346-2_50 |
55 | ″ | schema:sdDatePublished | 2022-06-01T22:36 |
56 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
57 | ″ | schema:sdPublisher | N8e72b81aaeb34254a79beb53b8b171a0 |
58 | ″ | schema:url | https://doi.org/10.1007/978-3-642-31346-2_50 |
59 | ″ | sgo:license | sg:explorer/license/ |
60 | ″ | sgo:sdDataset | chapters |
61 | ″ | rdf:type | schema:Chapter |
62 | N2ed4669085014871ac2ab93026a51dab | schema:name | dimensions_id |
63 | ″ | schema:value | pub.1035628918 |
64 | ″ | rdf:type | schema:PropertyValue |
65 | N413214a9b525406ba5c70cc20544040b | schema:familyName | Wang |
66 | ″ | schema:givenName | Jun |
67 | ″ | rdf:type | schema:Person |
68 | N425a52dfff5d46c9995339063816bf1b | schema:familyName | Polycarpou |
69 | ″ | schema:givenName | Marios M. |
70 | ″ | rdf:type | schema:Person |
71 | N52197d770f53495d8508a75f1771c0d0 | schema:familyName | Yen |
72 | ″ | schema:givenName | Gary G. |
73 | ″ | rdf:type | schema:Person |
74 | N5b3c261d4eee4ea3b63f2cef53b8f65c | rdf:first | sg:person.01136741416.72 |
75 | ″ | rdf:rest | rdf:nil |
76 | N7349809fe66e41ce8326efecc7214c1d | rdf:first | sg:person.013512526631.81 |
77 | ″ | rdf:rest | Ne011a813239a414c8f414ee0a786b242 |
78 | N785429d281a14d048b0ff39ca1e34ff2 | schema:isbn | 978-3-642-31345-5 |
79 | ″ | ″ | 978-3-642-31346-2 |
80 | ″ | schema:name | Advances in Neural Networks – ISNN 2012 |
81 | ″ | rdf:type | schema:Book |
82 | N8d034199143849b384c592f5c5e87f2e | schema:name | Springer Nature |
83 | ″ | rdf:type | schema:Organisation |
84 | N8e72b81aaeb34254a79beb53b8b171a0 | schema:name | Springer Nature - SN SciGraph project |
85 | ″ | rdf:type | schema:Organization |
86 | N9bc7eaea5da24af8a1eac3d9888c6537 | schema:name | doi |
87 | ″ | schema:value | 10.1007/978-3-642-31346-2_50 |
88 | ″ | rdf:type | schema:PropertyValue |
89 | Na3e257bfec26458c9678ce7a229bb1c4 | rdf:first | N413214a9b525406ba5c70cc20544040b |
90 | ″ | rdf:rest | Ncfd8158938c040b29df00c90b593f918 |
91 | Na4a5d2bc7c7f4b3088503160a57b47d4 | rdf:first | N425a52dfff5d46c9995339063816bf1b |
92 | ″ | rdf:rest | rdf:nil |
93 | Ncfd8158938c040b29df00c90b593f918 | rdf:first | N52197d770f53495d8508a75f1771c0d0 |
94 | ″ | rdf:rest | Na4a5d2bc7c7f4b3088503160a57b47d4 |
95 | Ne011a813239a414c8f414ee0a786b242 | rdf:first | sg:person.014111634523.11 |
96 | ″ | rdf:rest | N5b3c261d4eee4ea3b63f2cef53b8f65c |
97 | anzsrc-for:11 | schema:inDefinedTermSet | anzsrc-for: |
98 | ″ | schema:name | Medical and Health Sciences |
99 | ″ | rdf:type | schema:DefinedTerm |
100 | anzsrc-for:1103 | schema:inDefinedTermSet | anzsrc-for: |
101 | ″ | schema:name | Clinical Sciences |
102 | ″ | rdf:type | schema:DefinedTerm |
103 | sg:person.01136741416.72 | schema:affiliation | grid-institutes:grid.266673.0 |
104 | ″ | schema:familyName | Peng |
105 | ″ | schema:givenName | Yun |
106 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136741416.72 |
107 | ″ | rdf:type | schema:Person |
108 | sg:person.013512526631.81 | schema:affiliation | grid-institutes:grid.266673.0 |
109 | ″ | schema:familyName | Lo |
110 | ″ | schema:givenName | James Ting-Ho |
111 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013512526631.81 |
112 | ″ | rdf:type | schema:Person |
113 | sg:person.014111634523.11 | schema:affiliation | grid-institutes:grid.266673.0 |
114 | ″ | schema:familyName | Gui |
115 | ″ | schema:givenName | Yichuan |
116 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014111634523.11 |
117 | ″ | rdf:type | schema:Person |
118 | grid-institutes:grid.266673.0 | schema:alternateName | Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 21250, Baltimore, Maryland, USA |
119 | ″ | ″ | Department of Mathematics and Statistics, University of Maryland, Baltimore County, 21250, Baltimore, Maryland, USA |
120 | ″ | schema:name | Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 21250, Baltimore, Maryland, USA |
121 | ″ | ″ | Department of Mathematics and Statistics, University of Maryland, Baltimore County, 21250, Baltimore, Maryland, USA |
122 | ″ | rdf:type | schema:Organization |